CSE 326: Data Structures Network Flow
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CSE 326: Data StructuresNetwork Flow
James Fogarty
Autumn 2007
2
Network Flows
• Given a weighted, directed graph G=(V,E)
• Treat the edge weights as capacities
• How much can we flow through the graph?
A
C
B
D
FH
G
E
17
11
56
4
12
13
23
9
10
4I
611
20
3
Network flow: definitions
• Define special source s and sink t vertices
• Define a flow as a function on edges:– Capacity: f(v,w) <= c(v,w)– Conservation: for all u
except source, sink
– Value of a flow:
– Saturated edge: when f(v,w) = c(v,w)
Vv
vuf 0),(
v
vsff ),(
4
Network flow: definitions
• Capacity: you can’t overload an edge
• Conservation: Flow entering any vertex must equal flow leaving that vertex
• We want to maximize the value of a flow, subject to the above constraints
5
Network Flows
• Given a weighted, directed graph G=(V,E)
• Treat the edge weights as capacities
• How much can we flow through the graph?
s
C
B
D
FH
G
E
17
11
56
4
12
13
23
9
10
4t
611
20
6
A Good Idea that Doesn’t Work
• Start flow at 0• “While there’s room for more flow, push
more flow across the network!”– While there’s some path from s to t, none of
whose edges are saturated– Push more flow along the path until some
edge is saturated
– Called an “augmenting path”
7
How do we know there’s still room?
• Construct a residual graph: – Same vertices– Edge weights are the “leftover” capacity on
the edges– If there is a path st at all, then there is still
room
8
Example (1)
A
B C
D
FE
3
2
2
1
2
2
4
4
Flow / Capacity
Initial graph – no flow
9
Example (2)
A
B C
D
FE
0/3
0/2
0/2
0/1
0/2
0/2
0/4
0/4
Flow / CapacityResidual Capacity
3
2
4
1
2
4
2
2
Include the residual capacities
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Example (3)
1/3
0/2
0/2
1/1
0/2
0/2
0/4
1/4
Flow / CapacityResidual Capacity
2
2
4
0
2
3
2
2
A
B C
D
FE
Augment along ABFD by 1 unit (which saturates BF)
11
Example (4)
3/3
0/2
0/2
1/1
2/2
2/2
0/4
3/4
Flow / CapacityResidual Capacity
0
2
4
0
0
1
0
2
A
B C
D
FE
Augment along ABEFD (which saturates BE and EF)
12
Now what?
• There’s more capacity in the network…
• …but there’s no more augmenting paths
13
Network flow: definitions
• Define special source s and sink t vertices• Define a flow as a function on edges:
– Capacity: f(v,w) <= c(v,w)– Skew symmetry: f(v,w) = -f(w,v)– Conservation: for all u
except source, sink
– Value of a flow:
– Saturated edge: when f(v,w) = c(v,w)
Vv
vuf 0),(
v
vsff ),(
14
Network flow: definitions
• Capacity: you can’t overload an edge
• Skew symmetry: sending f from uv implies you’re “sending -f”, or you could “return f” from vu
• Conservation: Flow entering any vertex must equal flow leaving that vertex
• We want to maximize the value of a flow, subject to the above constraints
15
Main idea: Ford-Fulkerson method
• Start flow at 0• “While there’s room for more flow, push
more flow across the network!”– While there’s some path from s to t, none of
whose edges are saturated– Push more flow along the path until some
edge is saturated
– Called an “augmenting path”
16
How do we know there’s still room?
• Construct a residual graph: – Same vertices– Edge weights are the “leftover” capacity on
the edges– Add extra edges for backwards-capacity too!
– If there is a path st at all, then there is still room
17
Example (5)
3/3
0/2
0/2
1/1
2/2
2/2
0/4
3/4
Flow / CapacityResidual CapacityBackwards flow
0
2
4
0
0
1
0
2
2
1
2
3
3
A
B C
D
FE
Add the backwards edges, to show we can “undo” some flow
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Example (6)
3/3
2/2
2/2
1/1
0/2
2/2
2/4
3/4
Flow / CapacityResidual CapacityBackwards flow
0
0
2
0
0
1
2
0
2
1
2
3
3
A
B C
D
FE2
Augment along AEBCD (which saturates AE and EB, and empties BE)
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Example (7)
3/3
2/2
2/2
1/1
0/2
2/2
2/4
3/4
Flow / CapacityResidual CapacityBackwards flow
A
B C
D
FE
Final, maximum flow
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How should we pick paths?
• Two very good heuristics (Edmonds-Karp):– Pick the largest-capacity path available
• Otherwise, you’ll just come back to it later…so may as well pick it up now
– Pick the shortest augmenting path available• For a good example why…
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Don’t Mess this One Up
A
B
C
D
0/2000 0/2000
0/2000 0/2000
0/1
Augment along ABCD, then ACBD, then ABCD, then ACBD…
Should just augment along ACD, and ABD, and be finished
22
Running time?
• Each augmenting path can’t get shorter…and it can’t always stay the same length– So we have at most O(E) augmenting paths to
compute for each possible length, and there are only O(V) possible lengths.
– Each path takes O(E) time to compute
• Total time = O(E2V)
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Network Flows
• What about multiple turkey farms?
s
C
B
s
FH
G
E
17
11
56
4
12
13
23
9
10
4t
611
20
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Network Flows
• Create a single source, with infinite capacity edges connected to sources
• Same idea for multiple sinks
s
C
B
s
FH
G
E
17
11
56
4
12
13
23
9
10
4t
611
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s!
∞
∞
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One more definition on flows
• We can talk about the flow from a set of vertices to another set, instead of just from one vertex to another:
– Should be clear that f(X,X) = 0– So the only thing that counts is flow between
the two sets
Xx Yy
yxfYXf ),(),(
26
Network cuts
• Intuitively, a cut separates a graph into two disconnected pieces
• Formally, a cut is a pair of sets (S, T), such that
and S and T are connected subgraphs of G
{}
TS
TSV
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Minimum cuts
• If we cut G into (S, T), where S contains the source s and T contains the sink t,
• Of all the cuts (S, T) we could find, what is the smallest (max) flow f(S, T) we will find?
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Min Cut - Example (8)
A
B C
D
FE
3
2
2
1
2
2
4
4
TS
Capacity of cut = 5
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Coincidence?
• NO! Max-flow always equals Min-cut• Why?
– If there is a cut with capacity equal to the flow, then we have a maxflow:
• We can’t have a flow that’s bigger than the capacity cutting the graph! So any cut puts a bound on the maxflow, and if we have an equality, then we must have a maximum flow.
– If we have a maxflow, then there are no augmenting paths left• Or else we could augment the flow along that path, which would
yield a higher total flow.– If there are no augmenting paths, we have a cut of capacity
equal to the maxflow• Pick a cut (S,T) where S contains all vertices reachable in the
residual graph from s, and T is everything else. Then every edge from S to T must be saturated (or else there would be a path in the residual graph). So c(S,T) = f(S,T) = f(s,t) = |f| and we’re done.
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GraphCut
http://www.cc.gatech.edu/cpl/projects/graphcuttextures/
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